The discovery of new materials with novel chemical and physical properties often leads to the development of new and useful technologies. Traditionally, the discovery and development of materials has predominantly been a trial and error process carried out by scientists who generate data one experiment at a time. This process suffers from low success rates, long time lines, and high costs, particularly as the desired materials increase in complexity. There is currently a tremendous amount of activity directed towards the discovery and optimization of materials, such as superconductors, zeolites, magnetic materials, phosphors, catalysts, thermoelectric materials, high and low dielectric materials and the like. Unfortunately, even though the chemistry of extended solids has been extensively explored, few general principles have emerged that allow one to predict with certainty the composition, structure and/or reaction pathways for the synthesis of such solid state materials.
As a result, the discovery of new materials depends largely on the ability to synthesize and analyze large numbers of new materials. Given approximately 100 elements in the periodic table that can be used to make compositions consisting of two or more elements, an incredibly large number of possible new compounds remain largely unexplored, especially when processing variables are considered. One approach to the preparation and analysis of such large numbers of compounds has been the application of combinatorial chemistry.
In general, combinatorial chemistry refers to the approach of creating vast numbers of compounds by reacting a set of starting chemicals in all possible combinations. Since its introduction into the pharmaceutical industry in the late 1980's, it has dramatically sped up the drug discovery process and is now becoming a standard practice in that industry (Chem. Eng. News Feb. 12, 1996). More recently, combinatorial techniques have been successfully applied to the synthesis of inorganic materials (G. Briceno et al., SCIENCE 270, 273-275, 1995 and X. D. Xiang et al., SCIENCE 268, 1738-1740, 1995). By use of various surface deposition techniques, masking strategies, and processing conditions, it is now possible to generate hundreds to thousands of materials of distinct compositions per square inch. These materials include high Tc superconductors, magnetoresistors, and phosphors.
Using these techniques, it is now possible to create large libraries of diverse compounds or materials, including biomaterials, organics, inorganics, intermetallics, metal alloys, and ceramics, using a variety of sputtering, ablation, evaporation, and liquid dispensing systems as disclosed in U.S. Pat. Nos. 5,959,297, 6,004,617 and 6,030,917, which are incorporated by reference herein.
The generation of large numbers of new materials presents a significant challenge for conventional analytical techniques. By applying parallel or rapid serial screening techniques to these libraries of materials, however, combinatorial chemistry accelerates the speed of research, facilitates breakthroughs, and expands the amount of information available to researchers. Furthermore, the ability to observe the relationships between hundreds or thousands of materials in a short period of time enables scientists to make well-informed decisions in the discovery process and to find unexpected trends. High throughput screening techniques have been developed to facilitate this discovery process, as disclosed, for example, in U.S. Pat. Nos. 5,959,297, 6,030,917 and 6,034,775, which are incorporated by reference herein.
The vast quantities of data generated through the application of combinatorial and/or high throughput screening techniques can easily overwhelm conventional data acquisition, processing and management systems. Existing laboratory data management systems are ill-equipped to handle the large numbers of experiments required in combinatorial applications, and are not designed to rapidly acquire, process and store the large amount of data generated by such experiments, imposing significant limitations on throughput, both experimental and data processing, that stand in the way of the promised benefits of combinatorial techniques.
Basing laboratory data management systems on current relational or object-oriented databases leads to significant limitations. Those based on relational systems struggle to provide a facility for effectively defining and processing data that is intrinsically hierarchical in nature. Those based on current object-oriented databases struggle to offer the processing throughput necessary and/or may lack the flexibility of recomposition of the internal data or direct access into the internal structures of the objects as relational systems do with the relational view. Thus, there is a need for laboratory data management systems that combine the ability to process hierarchical data offered by object-oriented approaches and the processing power and/or flexibility of relational database systems.